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Research Article| Volume 17, ISSUE 3, P229-237, June 2023

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The indirect impact of the COVID-19 pandemic on people with type 2 diabetes mellitus and without COVID-19 infection: Systematic review and meta-analysis

  • Author Footnotes
    1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
    Zhuoran Hu
    Footnotes
    1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
    Search for articles by this author
  • Author Footnotes
    1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
    Hin Moi Youn
    Footnotes
    1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Jianchao Quan
    Affiliations
    School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Lily Luk Siu Lee
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Ivy Lynn Mak
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Esther Yee Tak Yu
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • David Vai-Kiong Chao
    Affiliations
    Department of Family Medicine and Primary Health Care, United Christian Hospital and Tseung Kwan O Hospital, Hong Kong Hospital Authority, Hong Kong Special Administrative Region of China
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  • Welchie Wai Kit Ko
    Affiliations
    Department of Family Medicine and Primary Health Care, Hong Kong Hospital Authority West Cluster, Hong Kong Special Administrative Region of China
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  • Ian Chi Kei Wong
    Affiliations
    Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region of China

    Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region of China

    Research Department of Practice and Policy, School of Pharmacy, University College London, London, United Kingdom

    Aston Pharmacy School, Aston University, Birmingham, United Kingdom
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  • Gary Kui Kai Lau
    Affiliations
    Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China

    State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Chak Sing Lau
    Affiliations
    Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Cindy Lo Kuen Lam
    Correspondence
    Corresponding author.
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
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  • Eric Yuk Fai Wan
    Correspondence
    Corresponding author at: Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China.
    Affiliations
    Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China

    Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region of China

    Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region of China
    Search for articles by this author
  • Author Footnotes
    1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
Published:March 01, 2023DOI:https://doi.org/10.1016/j.pcd.2023.02.006

      Highlights

      • Investigates the indirect impact of the pandemic on T2DM patients without infection.
      • Meta-analysis shows that hemoglobin A1c and body mass index changed insignificantly.
      • Meta-analysis shows that triglyceride and total cholesterol might worsen.

      Abstract

      Background

      The effect directly from the coronavirus disease 2019 (COVID-19) infection on health and fatality has received considerable attention, particularly among people with type 2 diabetes mellitus (T2DM). However, evidence on the indirect impact of disrupted healthcare services during the pandemic on people with T2DM is limited. This systematic review aims to assess the indirect impact of the pandemic on the metabolic management of T2DM people without a history of COVID-19 infection.

      Methods

      PubMed, Web of Science, and Scopus were systematically searched for studies that compared diabetes-related health outcomes between pre-pandemic and during-pandemic periods in people with T2DM and without the COVID-19 infection and published from January 1, 2020, to July 13, 2022. A meta-analysis was performed to estimate the overall effect on the diabetes indicators, including hemoglobin A1c (HbA1c), lipid profiles, and weight control, with different effect models according to the heterogeneity.

      Results

      Eleven observational studies were included in the final review. No significant changes in HbA1c levels [weighted mean difference (WMD), 0.06 (95% CI −0.12 to 0.24)] and body weight index (BMI) [0.15 (95% CI −0.24 to 0.53)] between the pre-pandemic and during-pandemic were found in the meta-analysis. Four studies reported lipid indicators; most reported insignificant changes in low-density lipoprotein (LDL, n = 2) and high-density lipoprotein (HDL, n = 3); two studies reported an increase in total cholesterol and triglyceride.

      Conclusions

      This review did not find significant changes in HbA1c and BMI among people with T2DM after data pooling, but a possible worsening in lipids parameters during the COVID-19 pandemic. There were limited data on long-term outcomes and healthcare utilization, which warrants further research.

      Systematic review registration

      PROSPERO CRD42022360433.

      Abbreviations:

      SARS-CoV-2 (coronavirus type 2), COVID-19 (coronavirus disease 2019), T2DM (type 2 diabetes mellitus), HbA1% (glycated hemoglobin), BMI (body mass index), BP (blood pressure), SBP/DBP (systolic pressure / diastolic pressure), TG (triglyceride), TC (total cholesterol), LDL (low-density lipoprotein), HDL (high-density lipoprotein), PICOS (population, intervention, control, outcome, and study design), 95% CI (95% confidence intervals), JBI (Joanna Briggs Institute), IQR (interquartile range), SD (standard deviation), WMD (weighted mean difference)

      Keywords

      1. Background

      The coronavirus disease 2019 (COVID-19) pandemic has been a major global public health emergency since December 2019, leading to a widespread shift in healthcare priorities [
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      WHO, The Impact of the COVID-19 Pandemic on Noncommunicable Disease Resources and Services: Results of A Rapid Assessment, 2020.

      ].
      The indirect effect of the pandemic on T2DM mostly arises from changes in routine care, such as disrupted delivery and the use of health services. During the pandemic, non-essential, non-emergent, elective health services were postponed or canceled in response to the pandemic [
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      ]. People were reluctant to seek medical assistance due to fear of risking exposure to the infection [
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      Diabetes and COVID-19: risks, management, and learnings from other national disasters.
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      ]. For people with T2DM who require consistent monitoring of symptoms and timely treatment adjustment, such as personalized glycemic control protocol [
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      ], the disruption of care created new barriers to managing the disease, which may have resulted in poor health outcomes or incidence of diabetes-related complications. The indirect impact is also recognized from alterations in daily life due to infection control measures, such as lockdowns and social distancing. Decreased mobility or physical activities, increased psychological stress, and changed food intake pose additional challenges to retaining adequate diabetes management and usual healthy habits, which would potentially affect diabetes control of glycemic level, blood lipid, and weight during the pandemic [
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      ]. However, the extent of the indirect consequences on people with T2DM and without a history of the infection is yet to be investigated. Considering that health resources were mostly diverted to the care of those with COVID-19 during the early period of the pandemic [
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      ], those without the infection might be less convenient to access health services. Thus, in this review, we focus on people with T2DM who do not have a history of the infection to eliminate factors that could potentially affect the outcome, aiming to evaluate the true indirect impact of the pandemic on people with T2DM by reviewing studies that specifically target those not infected with COVID-19.

      2. Materials and methods

      2.1 Data sources and searching strategy

      This systematic review was conducted using PICOS (Population, Intervention, Comparison, Outcome, and Study design) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) tools. We systematically searched PubMed, Web of Science Core Collection, and Scopus published between January 2020 and July 2022, using Medical Subject Headings (MeSH) and text search terms “diabetes mellitus, type 2” [MeSH] AND “COVID-19” [MeSH] OR “SARS-CoV-2” [MeSH] OR coronavirus [MeSH] AND (“glycemic control” [MeSH] OR ‘indirect*’ [tiab] OR ‘spill-over*’ [tiab]). The detailed search strategy is provided in Supplementary Tables 1 and 2. In addition, we used Google Scholar to manually conduct a forward search of included studies until July 13, 2022.

      2.2 Study selection

      We included studies that (1) targeted people who were diagnosed with T2DM, (2) excluded people who had COVID-19 infection (3) compared diabetes-related outcomes between pre-pandemic and during-pandemic periods. Studies were excluded if they included subjects with other types of diabetes or confirmed COVID-19 cases. We excluded studies that measured non-diabetes-related health outcomes, such as mental health.

      2.3 Screening process, quality assessment, and data extraction

      Using these eligibility criteria, two independent investigators (HZR and LSL) screened all studies at the titles and abstracts and subsequently performed full-text screening to ensure that all primarily selected studies were satisfied with the selection criteria. Two independent investigators (HZR and YHM) assessed the methodological quality using Joanna Briggs Institute (JBI) Critical Appraisal Tools [
      • Aromataris E.
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      JBI Manual For Evidence Synthesis.
      ] for cohort assessment of the research quality (supplementary Table 3) and conducted the data extraction.

      2.4 Outcomes of interest

      The primary outcome was glycemic control, including HbA1c and mean fasting blood glucose if HbA1c was not reported. Although glycemic control is the center of diabetes management, as a metabolic disease, a good prognosis of diabetes people still requires a multiple-dimensional control of metabolic parameters because the high prevalence of hypertension and dyslipidemia in diabetes people [
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      Association of fenofibrate therapy with long-term cardiovascular risk in statin-treated patients with type 2 diabetes.
      ] requires physicians to monitor and control blood pressure and lipid. We extracted the data as secondary outcomes if the included studies reported (1) body weight (kg) or BMI (kg/m2), (2) lipid profile (mg/dL) included triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL) and (3) blood pressure (BP, mmHg). Moreover, only the pre-pandemic and during-pandemic data from the same study subjects were extracted (supplementary Fig. 1).

      2.5 Data synthesis and analysis

      We performed meta-analyses for each dependent outcome, including HbA1c, BMI, and lipid profiles, to assess the indirect impact of COVID-19 on people with T2DM. Inverse-variance weighting mean difference (WMD) was calculated separately for parameters extracted from before and during the COVID-19 pandemic. For studies that measured and reported outcomes multiple times during the investigation period, we selected the last measurement because it covered the whole length of observation.
      Statistical heterogeneity between the studies was assessed before choosing effect models for WMD. I2 values of > 50% and a significant Cochran Q-test (p < 0.05) indicate significant inconsistency. Publication bias was assessed using funnel plots and Egger’s tests [
      • Egger M.
      • Smith G.D.
      • Schneider M.
      • Minder C.
      Bias in meta-analysis detected by a simple, graphical test.
      ] (if >=10 studies were selected). We used the “trim-and-fill” method [
      • Duval S.
      • Tweedie R.
      Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis.
      ] to adjust the funnel plot and recalculated the results if there was publication bias. Sensitivity analyses were also conducted using the leave-one-out strategy to examine whether a single study could have markedly affected the results’ stability. Univariable and multivariable meta-regression analyses were conducted to discover which factors brought high heterogeneity. If the heterogeneity is still significant after removing the influential studies based on the sensitivity analysis, we performed subgroup analyses using significant moderators obtained from the meta-regression as grouping factors. Any discrepancies were resolved through discussion with another investigator (WYF). All median values with interquartile range (IQR) [
      • D’Onofrio L.
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      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Rastogi A.
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      Improved glycemic control amongst people with long-standing diabetes during COVID-19 lockdown: a prospective, observational, nested cohort study.
      ] or extremum were converted to mean and standard deviation (SD) [
      • Wan X.
      • Wang W.
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      • Tong T.
      Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.
      ]. All parameters were reported with the same units. The statistical significance level was set at p < 0.05. For all outcomes, 95% confidence intervals (CIs) were calculated. We performed the analysis using the R (version 4.1.0) package ‘metafor’ and showed the meta-analysis results with forest plots. No missing data were identified among included studies.

      3. Results

      3.1 Literature collection and general data

      The PRISMA flowchart in Fig. 1 summarizes the search results and selection process of all studies included in the analysis. Overall, the number of records identified by our database searches was 6506. Of these records, 223 were screened for the full-text review after removing 6263 studies from the title and abstract screening process. Finally, a total of eleven studies were included in the final review.
      Table 1 presents the clinical characteristics. Eleven studies provide clinical data from an aggregate sample of 3192 subjects in 6 countries. The mean age of the subjects was between 50 and 80 years. Nine studies measured the lockdown where duration and restrictions vary depending on countries. Turkey, for example, enforced a lockdown for three months from March to June 2020. The investigation period ranged between 1 and 8 months after the first wave of the pandemic or the lockdown initiation.
      Table 1Clinical characteristics of included studies.
      SourceCountryStudy designSample size, nMale, %Mean age ± SD, yearsControl

      (Pre-pandemic)
      SpanEndpoint

      (Pandemic)
      Outcome
      Glycemic parameterLipid profileBMI
      Munekawa et al., Sep 2020Kyoto, JapanRetrospective cohort18362.167.4 ± 11.3Jan-Feb, 20203monthsApr-May 2020IncreasedNo significant change*
      Rastogi et al., Oct 2020Chandigarh, IndiaProspective cohort44271.8median (range)

      58 (52–64)
      Pre-lockdownlockdown68daysImmediately after the lockdown liftedDecreasedNo significant change
      Onmez et al., Oct 2020Duzce, TurkeyRetrospective†10156.555 ± 13Pre-lockdownlockdown75days1 month after the lockdown liftedNo significant changeNo significant change
      Karatas et al. Jan 2021Istanbul, TurkeyCase-control†8531.855.8 ± 10.5Pre-lockdown6months6 months after

      pre-lockdown
      IncreasedIncreasedIncreased
      D’Onofrio et al. Mar 2021Lazio, ItalyRetrospective, case-control†14161median (range)

      68 (61–74)
      December 9 2019 –

      March 9 2020
      lockdown61days1 month after the lockdown liftedNo significant differenceNo significant differenceNo significant difference
      Falcetta et al. Mar 2021Pisa, ItalyRetrospective cohort3046569.1 ± 9.2Pre-lockdownlockdown61days1 month after the lockdown liftedNo significant differenceDecreasedIncreased
      Biamonte et al. Jun 2021Rozzano, ItalyRetrospective†12857.8median (range)

      70 (40–91)
      Pre-lockdownlockdown61days1 month after the lockdown liftedIncreasedIncreased
      Verma et al. Oct 2021Bathinda, IndiaCross-sectional†2061.251.5 ± 11.3Pre-lockdownlockdown68days1–3 months after lockdown liftedDecreased*
      Jin et al. Oct 2021Daegu, KoreaRetrospective†99660.562.9 ± 11.3January 1–18 Feb 20206monthsJul- Aug 2020Increased
      Selek et al. Dec 2021Kocaeli, TurkeyRetrospective†2834758.0 ± 12.1Pre-pandemiclockdown75days15 days - 5 months after lockdown liftedNo significant differenceIncreased*No significant difference
      Alowainati et al. Dec 2021Doha, QatarRetrospective†50941.355.8 ± 13.4Pre-lockdownlockdown3monthsImmediately after the lockdown liftedNo significant difference
      *: body weight or mean fasting glucose. Unless otherwise specified: glycemic parameter means HbA1c (%), body weight means BMI (Kg/m2); lipid profile (mg/dL) included triglyceride, total cholesterol, low-density lipoprotein, and high-density lipoprotein, HDL. †: included cohort follow-up data. Only extracted and analyzed the cohort data.
      Lockdown policies in different countries were different.
      All studies reported clinical parameters (Fig. 2), eleven reported glycemic control indicators, six reported BMI, four reported lipid profiles, and one reported blood pressure. No significant changes were found in most included studies. Except for glycemic parameters, LDL and TC, no improvements were reported in the remaining parameters.
      Fig. 2
      Fig. 2Changes in clinical parameters compared with the pre-pandemic HbA1%: glycated hemoglobin; BP: blood pressure; BMI: body mass index; TC: total cholesterol; TG: triglyceride; LDL: low-density lipoprotein; HDL: high-density lipoprotein.

      3.2 Effect on HbA1c

      The results of the systematic review showed that the COVID-19 outbreak did not significantly change HbA1c (%) (n = 5) [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Önmez A.
      • Gamsızkan Z.
      • Özdemir Ş.
      • Kesikbaş E.
      • Gökosmanoğlu F.
      • Torun S.
      • Cinemre H.
      The effect of COVID-19 lockdown on glycemic control in patients with type 2 diabetes mellitus in Turkey.
      ,
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ,
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ,
      • Alowainati B.
      • Dabbous Z.
      • Salameh O.
      • Hamad M.
      • Al Hail L.
      • Abuheliqa W.
      • AL-Janahi I.
      • Jayyousi A.
      • Zirie M.
      The impact of lockdown and changes in clinical practice on glycemic control during the COVID-19 pandemic: analysis of data from the National Diabetes Center, Doha, Qatar.
      ]. Deteriorated HbA1c (%) (n = 4) [
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ,
      • Munekawa C.
      • Hosomi Y.
      • Hashimoto Y.
      • Okamura T.
      • Takahashi F.
      • Kawano R.
      • Nakajima H.
      • Osaka T.
      • Okada H.
      • Majima S.
      • Senmaru T.
      • Nakanishi N.
      • Ushigome E.
      • Hamaguchi M.
      • Yamazaki M.
      • Fukui M.
      Effect of coronavirus disease 2019 pandemic on the lifestyle and glycemic control in patients with type 2 diabetes: a cross-section and retrospective cohort study.
      ,
      • Biamonte E.
      • Pegoraro F.
      • Carrone F.
      • Facchi I.
      • Favacchio G.
      • Lania A.G.
      • Mazziotti G.
      • Mirani M.
      Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect.
      ,
      • Jin J.
      • Lee S.W.
      • Lee W.-K.
      • Jeon J.-H.
      • Kim J.-G.
      • Lee I.-K.
      • Choi Y.-K.
      • Park K.-G.
      Year-long trend in glycated hemoglobin levels in patients with type 2 diabetes during the COVID-19 pandemic.
      ] were reported in Japan and Korea, which did not implement lockdown, and in Italy and Turkey, which imposed strict lockdown regulations. Studies from India all showed improved HbA1c% [
      • Rastogi A.
      • Hiteshi P.
      • Bhansali A.
      Improved glycemic control amongst people with long-standing diabetes during COVID-19 lockdown: a prospective, observational, nested cohort study.
      ] and blood glucose [
      • Verma M.
      • Sharma P.
      • Chaudhari A.
      • Sharma M.
      • Kalra S.
      Effect of lockdown on diabetes care during the COVID-19 pandemic: result of a telephone-based survey among patients attending a diabetic clinic in northern India.
      ].
      The meta-analysis pooled data and showed no significant change in HbA1c levels during the COVID-19 period compared with the pre-pandemic, with a WMD of 0.06 (95% CI −0.12 to 0.24), p = 0.544, I2= 78.2% based on ten papers (Fig. 3). No significant publication bias was observed in the funnel plots and Egger’s test (p = 0.695, Supplement Fig. 2). Leave-one-out sensitivity analysis demonstrated that no included studies were required to be removed.
      Fig. 3
      Fig. 3Meta-analysis to assess the indirect impact of COVID-19 on HbA1c (%).
      Multivariable meta-regression analysis showed that study location (odd ratio, OR: 1.73, 95% CI 1.05–2.85, p < 0.05) and lockdown enforcement (OR: 0.55, 95% CI 0.31–0.98, p < 0.05) were interactively associated with HbA1c. However, study location (subgroup difference, p = 0.18) and lockdown implementation (subgroup difference, p = 0.79) did not affect HbA1c significantly after the pandemic after subgroup analysis.

      3.3 Effect on lipid profiles

      Fig. 4 shows the results of the meta-analysis for blood lipids. Lipid panel (mg/dl) was reported in 4 European studies. Two studies on TC showed improvement [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ] with a significant WMD of − 6.26 (95%CI −10.69 to −1.93). TC is a composite of HDL, LDL, and TG levels []. We performed a further meta-analysis on TG, HDL, and LDL for two studies that reported TC [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ], showing that the decrease in TC could be attributed to the decrease in HDL (Supplementary Fig. 6b). For TG, deterioration [
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ,
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ] and stability [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ] were equally reported, but its recalculated WMD suggests a significant increase (16.34, 95%CI 5.10 – 27.58) after removing D’Onofrio et al. [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ] based on the sensitivity analysis for reducing the potential publication bias.
      Fig. 4
      Fig. 4Meta-analysis to assess the indirect impact of COVID-19 on lipid parameters. *: converted from median (range).
      Improvement [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ] in LDL was more than deterioration [
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ] and stability [
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ], while the pooled data using the random-effect model showed no significant changes (WMD: 20.91, 95% CI −8.89 to 50.71). No improvements in HDL were reported in the included papers (stable [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ,
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ]; decreased [
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ]) with an insignificant WMD: 1.18 (95% CI −0.26 to 2.61). Funnel plots and details of the analysis are shown in Supplementary Figs. 5–8.

      3.4 Effect on BMI and BP

      Six studies measured BMI or body weight [
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ,
      • Munekawa C.
      • Hosomi Y.
      • Hashimoto Y.
      • Okamura T.
      • Takahashi F.
      • Kawano R.
      • Nakajima H.
      • Osaka T.
      • Okada H.
      • Majima S.
      • Senmaru T.
      • Nakanishi N.
      • Ushigome E.
      • Hamaguchi M.
      • Yamazaki M.
      • Fukui M.
      Effect of coronavirus disease 2019 pandemic on the lifestyle and glycemic control in patients with type 2 diabetes: a cross-section and retrospective cohort study.
      ]; four of six also reported waist circumstances (WC). Of six studies, three showed significant deterioration in BMI [
      • Falcetta P.
      • Aragona M.
      • Ciccarone A.
      • Bertolotto A.
      • Campi F.
      • Coppelli A.
      • Dardano A.
      • Giannarelli R.
      • Bianchi C.
      • Del Prato S.
      Impact of COVID-19 lockdown on glucose control of elderly people with type 2 diabetes in Italy.
      ,
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ,
      • Biamonte E.
      • Pegoraro F.
      • Carrone F.
      • Facchi I.
      • Favacchio G.
      • Lania A.G.
      • Mazziotti G.
      • Mirani M.
      Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect.
      ] but gained an insignificant mean difference [0.15 (95% CI −0.24 to 0.53)] from the fixed-effect model (Fig. 5). However, potential publication bias can be observed in the funnel plots and Egger’s test (p = 0.008). After adjusting the funnel plot with the ‘trim-and-fill’ method, the recalculated WMD is 0.04 (95% CI −0.31 to 0.39). The WMD of WC [0.08 (95% CI −0.68 to 2.29)] also shows no significance (Supplementary Fig. 9).
      Fig. 5
      Fig. 5Meta-analysis to assess the indirect impact of COVID-19 on BMI (Kg/m2). *: converted from median (range).
      One study reported that the self-reported proportion of poorly controlled hypertension increased (pre-COVID-19 vs. COVID-19: 22% vs. 27%, p < 0.001), but without quantitative data [
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ].

      4. Discussion

      During the COVID-19 pandemic, various measures to control the spread of the virus have affected the elective healthcare and daily life of people with T2DM. While most studies have examined health effects directly related to COVID-19, this is the first systematic review to investigate the indirect impact of the pandemic explicitly in T2DM people without a history of the infection. A total of 11 studies that satisfied the criteria were included in the final analysis. Overall, we found no significant changes in HbA1c% and BMI between pre-pandemic and during-pandemic but significant changes in TC and TG from lipid panels.
      The previous systematic reviews and meta-analysis have shown inconsistent results regarding changes in the level of glycated hemoglobin and lipid profiles in diabetes people during the pandemic. For example, Ojo et al. [
      • Ojo O.
      • Wang X.-H.
      • Ojo O.O.
      • Orjih E.
      • Pavithran N.
      • Adegboye A.R.A.
      • Feng Q.-Q.
      • McCrone P.
      The effects of COVID-19 lockdown on glycaemic control and lipid profile in patients with type 2 diabetes: a systematic review and meta-analysis.
      ] found a significantly increased level of HbA1c (OR 0.34, 95% CI 0.03 – 0.38, p < 0.05), while Silverii et al. [
      • Silverii G.A.
      • Delli Poggi C.
      • Dicembrini I.
      • Monami M.
      • Mannucci E.
      Glucose control in diabetes during home confinement for the first pandemic wave of COVID-19: a meta-analysis of observational studies.
      ] (OR −1.26, 95% CI −3.91 to 1.39, in mmol/L) and Eberle et al. [
      • Eberle C.
      • Stichling S.
      Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus: a systematic review.
      ] (OR 0.14, 95% CI −0.13 to 0.40, p > 0.05) found no statistically significant changes. Similar to our findings, Ojo’s review also illustrated improvements in lipid profiles, with TC (OR −0.53, 95% CI −0.56 to −0.50), TG (OR −0.06, 95% CI −0.09 to −0.04), LDL (OR −0.11, 95% CI −0.56 to −0.50), and HDL (OR 3.69, 95% CI 3.27 4.11). The difference in study designs may explain the inconsistency in the findings of our review compared with the previous ones. The primary difference is that we only included the studies where participants were strictly selected without the COVID-19 infection because it could affect the outcome when evaluating the indirect impact of the pandemic. People with T2DM and COVID-19 infection may have fewer barriers to accessing health services than those without the infection, potentially mitigating the effect of disruptions in care [
      • Williams R.
      • Jenkins D.A.
      • Ashcroft D.M.
      • Brown B.
      • Campbell S.
      • Carr M.J.
      • Cheraghi-Sohi S.
      • Kapur N.
      • Thomas O.
      • Webb R.T.
      • Peek N.
      Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study.
      ]. Moreover, the SARS-CoV-2 would activate immune factors, leading to multiple organ injuries, especially the liver, and pancreas, which regulate blood sugar [
      • Ni W.
      • Yang X.
      • Yang D.
      • Bao J.
      • Li R.
      • Xiao Y.
      • Hou C.
      • Wang H.
      • Liu J.
      • Yang D.
      • Xu Y.
      • Cao Z.
      • Gao Z.
      Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19.
      ,
      • Ilias I.
      • Diamantopoulos A.
      • Pratikaki M.
      • Botoula E.
      • Jahaj E.
      • Athanasiou N.
      • Tsipilis S.
      • Zacharis A.
      • Vassiliou A.G.
      • Vassiliadi D.A.
      • Kotanidou A.
      • Tsagarakis S.
      • Dimopoulou I.
      Glycemia, beta-cell function and sensitivity to insulin in mildly to critically Ill covid-19 patients.
      ]. Another difference is that most review studies compared pre-pandemic and during-pandemic data from different subjects, but we only extracted the studies which followed up on the same subjects generating conservative results.

      4.1 Effect on HbA1c and BMI

      Although no significant changes in HbA1c and BMI in people with T2DM were observed, this may be possibly attributed to variations in pandemic containment policies and implementation approaches across different settings. First, lockdown measures differ in each country regarding stringent levels or durations. For example, Italy [
      • Becchetti L.
      • Conzo G.
      • Conzo P.
      • Salustri F.
      Understanding the heterogeneity of COVID-19 deaths and contagions: the role of air pollution and lockdown decisions.
      ] and Turkey [
      • Gobbi E.
      • Maltagliati S.
      • Sarrazin P.
      • di Fronso S.
      • Colangelo A.
      • Cheval B.
      • Escriva-Boulley G.
      • Tessier D.
      • Demirhan G.
      • Erturan G.
      • Yüksel Y.
      • Papaioannou A.
      • Bertollo M.
      • Carraro A.
      Promoting physical activity during school closures imposed by the first wave of the COVID-19 pandemic: physical education teachers’ behaviors in France, Italy and Turkey.
      ] imposed a stricter curfew during the daytime, whereas Belgium only had mild restrictions for outdoor activities [
      • Schmidt S.C.E.
      • Anedda B.
      • Burchartz A.
      • Eichsteller A.
      • Kolb S.
      • Nigg C.
      • Niessner C.
      • Oriwol D.
      • Worth A.
      • Woll A.
      Physical activity and screen time of children and adolescents before and during the COVID-19 lockdown in Germany: a natural experiment.
      ]. Moreover, various lockdown and social restrictions measures were introduced and lifted in order to adapt to the constantly changing situations during the pandemic [
      • Ayouni I.
      • Maatoug J.
      • Dhouib W.
      • Zammit N.
      • Fredj S.B.
      • Ghammam R.
      • Ghannem H.
      Effective public health measures to mitigate the spread of COVID-19: a systematic review.
      ]. For example, a lockdown in India mandated not leaving home at the peak of the COVID-19 pandemic but gradually eased and maintained a semi-lockdown about two weeks after the declaration of national lock, allowing people to go out for medical and other necessary activities [
      • Padmakumar A.
      • Patil G.R.
      COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities.
      ]. Second, the variation in investigation periods to assess HbA1c may also affect the results. The follow-up periods in the included studies varied from 1 to 8 months, but HbA1c is commonly used to monitor at least three months of glycemic control among diabetes people [
      • Nathan D.M.
      • Kuenen J.
      • Borg R.
      • Zheng H.
      • Schoenfeld D.
      • Heine R.J.
      Translating the A1C assay into estimated average glucose values.
      ]. Therefore, an extended investigation period may be able to provide more information. For example, a significant deterioration of HbA1c was observed in the studies with a 6-month investigation period [
      • Karatas S.
      • Yesim T.
      • Beysel S.
      Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people.
      ,
      • Jin J.
      • Lee S.W.
      • Lee W.-K.
      • Jeon J.-H.
      • Kim J.-G.
      • Lee I.-K.
      • Choi Y.-K.
      • Park K.-G.
      Year-long trend in glycated hemoglobin levels in patients with type 2 diabetes during the COVID-19 pandemic.
      ], even though the pooled WMD was insignificant [0.36, 95% CI (−0.25 to 0.97), Supplementary Fig. 4], suggesting future studies with a long observational period with a large study population are required.

      4.2 Effect on lipid panel

      Increased TG and declined HDL are well-recognized characteristics of T2DM dyslipidemia [
      • Beckman J.A.
      • Creager M.A.
      • Libby P.
      Diabetes and atherosclerosis: epidemiology, pathophysiology, and management.
      ], and lipid panels can convey another insight into glycemic control due to a positive association between TG/HDL ratio, insulin resistance [
      • Quispe R.
      • Martin S.S.
      • Jones S.R.
      Triglycerides to high-density lipoprotein-cholesterol ratio, glycemic control and cardiovascular risk in obese patients with type 2 diabetes.
      ], and glycemic under control [
      • Babic N.
      • Valjevac A.
      • Zaciragic A.
      • Avdagic N.
      • Zukic S.
      • Hasic S.
      The triglyceride/HDL ratio and triglyceride glucose index as predictors of glycemic control in patients with diabetes mellitus type 2.
      ,
      • Hussain A.
      • Ali I.
      • Ijaz M.
      • Rahim A.
      Correlation between hemoglobin A1c and serum lipid profile in Afghani patients with type 2 diabetes: hemoglobin A1c prognosticates dyslipidemia.
      ] observed in people with T2DM. Moreover, higher circulating TG has been reported to cause an elevated level of HbA1c [
      • Hsiung C.-N.
      • Chang Y.-C.
      • Lin C.-W.
      • Chang C.-W.
      • Chou W.-C.
      • Chu H.-W.
      • Su M.-W.
      • Wu P.-E.
      • Shen C.-Y.
      The causal relationship of circulating triglyceride and glycated hemoglobin: a Mendelian randomization study.
      ], suggesting TG may be a more sensitive indicator for suboptimal glycemic control. Thus, after meta-analysis, the increased TG and decreased TC, which might be attributed to the declined HDL, may indicate the pandemic’s adverse impact on glycemic control. However, more studies on the pandemic effect of blood lipids are excepted since the limited number of included studies may decrease the power of this meta-analysis, especially for TC.

      4.3 Effect on healthcare disruption

      Disruption of healthcare is a widely discussed impact of the pandemic. A universally decreased health service utilization for non-COVID-19 conditions [
      • Roy C.M.
      • Bollman E.B.
      • Carson L.M.
      • Northrop A.J.
      • Jackson E.F.
      • Moresky R.T.
      Assessing the indirect effects of COVID-19 on healthcare delivery, utilization and health outcomes: a scoping review.
      ] has been reported in many countries [
      • Hartmann-Boyce J.
      • Morris E.
      • Goyder C.
      • Kinton J.
      • Perring J.
      • Nunan D.
      • Mahtani K.
      • Buse J.B.
      • Del Prato S.
      • Ji L.
      • Roussel R.
      • Khunti K.
      Diabetes and COVID-19: risks, management, and learnings from other national disasters.
      ,
      • Leite N.J.C.
      • Raimundo A.M.M.
      • Mendes R.D.C.
      • Marmeleira J.F.F.
      Impact of COVID-19 pandemic on daily life, physical exercise, and general health among older people with type 2 diabetes: a qualitative interview study.
      ,

      Preliminary estimate of excess mortality during the COVID-19 outbreak – New York City, March 11–May 2, 2020, MMWR Morb. Mortal. Wkly. Rep., 69, 2020, pp. 603–605.

      ,
      • Dai Z.
      • Wang J.J.
      • Shi J.J.
      How does the hospital make a safe and stable elective surgery plan during COVID-19 pandemic?.
      ], characterized by reduced health service hours due to lockdowns, dwindling human resource availability due to medical source re-allocation, and hesitancy to seek care due to fears of contracting the virus during the pandemic; such change may result in challenging management of diabetes in the long term, even though it takes time to be revealed. One study [
      • Wang S.Y.
      • Chen L.K.
      • Hsu S.H.
      • Wang S.C.
      Health care utilization and health outcomes: a population study of Taiwan.
      ] examined the impact of the 2003 SARS epidemic retrospectively and found a short-term dramatic decline in healthcare utilization would lead to increased diabetic mortality one year after the outbreak [relative risk:1.08 (CI: 1.03 – 1.15)]. This previous experience raises similar concerns during the present pandemic, as the present pandemic has lasted more extended time and affected more globally than the SARS epidemic, with the on-and-off disease-control policies, like repeated lockdown implementation when facing emerging virus variants [
      • Schaffler Y.
      • Kaltschik S.
      • Probst T.
      • Jesser A.
      • Pieh C.
      • Humer E.
      Mental health in Austrian psychotherapists during the COVID-19 pandemic.
      ,

      Department of Health and Social Care UK, COVID-19 Response – 2021 Spring. Available: 〈https://www.gov.uk/government/publications/covid-19-response-spring-2021〉, (Accessed February 2023).

      ]. This study provides a window to view if disruptions in care or re-allocation of resources and daily life alterations may have affected diabetes management, informing in response to this burden. Although our results did not significantly impact glycemic and weight parameters in people with T2DM, possibly attributable to the limited data and modifications, some findings from lipid profiles send a wake-up call. Further studies are required, and developing a concrete response to improve the adaptability of healthcare systems and the daily life aid for diabetes populations will be crucial for similar future events.

      4.4 Effect on lifestyle changes

      Lockdown and social distancing during the pandemic could negatively affect physical activity and diet habits, as shown by three included studies [
      • Önmez A.
      • Gamsızkan Z.
      • Özdemir Ş.
      • Kesikbaş E.
      • Gökosmanoğlu F.
      • Torun S.
      • Cinemre H.
      The effect of COVID-19 lockdown on glycemic control in patients with type 2 diabetes mellitus in Turkey.
      ,
      • Selek A.
      • Gezer E.
      • Altun E.
      • Sözen M.
      • Topaloğlu Ö.
      • Köksalan D.
      • Demirkan H.
      • Karakaya D.
      • Cetinarslan B.
      • Cantürk Z.
      • Taymez D.
      The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli.
      ,
      • Biamonte E.
      • Pegoraro F.
      • Carrone F.
      • Facchi I.
      • Favacchio G.
      • Lania A.G.
      • Mazziotti G.
      • Mirani M.
      Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect.
      ] and others [
      • Ammar A.
      • Brach M.
      • Trabelsi K.
      • Chtourou H.
      • Boukhris O.
      • Masmoudi L.
      • Bouaziz B.
      • Bentlage E.
      • How D.
      • Ahmed M.
      • Müller P.
      • Müller N.
      • Aloui A.
      • Hammouda O.
      • Paineiras-Domingos L.L.
      • Braakman-Jansen A.
      • Wrede C.
      • Bastoni S.
      • Pernambuco C.S.
      • Mataruna L.
      • Taheri M.
      • Irandoust K.
      • Khacharem A.
      • Bragazzi N.L.
      • Chamari K.
      • Glenn J.M.
      • Bott N.T.
      • Gargouri F.
      • Chaari L.
      • Batatia H.
      • Ali G.M.
      • Abdelkarim O.
      • Jarraya M.
      • Abed K.E.
      • Souissi N.
      • Van Gemert-Pijnen L.
      • Riemann B.L.
      • Riemann L.
      • Moalla W.
      • Gómez-Raja J.
      • Epstein M.
      • Sanderman R.
      • Schulz S.V.
      • Jerg A.
      • Al-Horani R.
      • Mansi T.
      • Jmail M.
      • Barbosa F.
      • Ferreira-Santos F.
      • Šimunič B.
      • Pišot R.
      • Gaggioli A.
      • Bailey S.J.
      • Steinacker J.M.
      • Driss T.
      • Hoekelmann A.
      Effects of COVID-19 home confinement on eating behaviour and physical activity: results of the ECLB-COVID19 international online survey.
      ,
      • Batlle-Bayer L.
      • Aldaco R.
      • Bala A.
      • Puig R.
      • Laso J.
      • Margallo M.
      • Vázquez-Rowe I.
      • Antó J.M.
      • Fullana-I-Palmer P.
      Environmental and nutritional impacts of dietary changes in Spain during the COVID-19 lockdown.
      ,
      • Ferrante G.
      • Camussi E.
      • Piccinelli C.
      • Senore C.
      • Armaroli P.
      • Ortale A.
      • Garena F.
      • Giordano L.
      Did social isolation during the SARS-CoV-2 epidemic have an impact on the lifestyles of citizens?.
      ]. Nevertheless, there is a possibility that people with diabetes rethink their lifestyles and compensate for decreased physical activity by enhancing medication adherence [
      • Verma M.
      • Sharma P.
      • Chaudhari A.
      • Sharma M.
      • Kalra S.
      Effect of lockdown on diabetes care during the COVID-19 pandemic: result of a telephone-based survey among patients attending a diabetic clinic in northern India.
      ] and developing healthier and more nutritious dietary habits [
      • D’Onofrio L.
      • Pieralice S.
      • Maddaloni E.
      • Mignogna C.
      • Sterpetti S.
      • Coraggio L.
      • Luordi C.
      • Guarisco G.
      • Leto G.
      • Leonetti F.
      • Manfrini S.
      • Buzzetti R.
      Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study.
      ,
      • Di Renzo L.
      • Gualtieri P.
      • Pivari F.
      • Soldati L.
      • Attinà A.
      • Cinelli G.
      • Leggeri C.
      • Caparello G.
      • Barrea L.
      • Scerbo F.
      • Esposito E.
      • De Lorenzo A.
      Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey.
      ,
      • Lehtisalo J.
      • Palmer K.
      • Mangialasche F.
      • Solomon A.
      • Kivipelto M.
      • Ngandu T.
      Changes in lifestyle, behaviors, and risk factors for cognitive impairment in older persons during the first wave of the coronavirus disease 2019 pandemic in Finland: results from the FINGER study.
      ]. In particular, the included studies were conducted during the first wave outbreak when people were most concerned about infection. More quantitative evidence on lifestyle changes and its consequence on glycemic control is needed because of the conflicting results.

      4.5 Strengths and limitations

      The unprecedented situation in diabetes care calls for prioritizing efforts to address any challenges for effective disease management. This review provides the first overview of the indirect impact of the COVID-19 pandemic on people with T2DM without infection. We adjusted potential publication bias and improved the robustness of the meta-analysis by calculating the mean difference with conservative effect models and heterogeneity-lowering methods.
      This review had several limitations. First, all available evidence included in the review was assessed during the early period of the pandemic. The long-term impact caused by disease-control policies during the early stages of the pandemic remains to be determined. For instance, the healthcare disruption may predispose diabetes patients to a greater risk of future marco- and micro cardiovascular complications [
      • Mak I.L.
      • Wan E.Y.F.
      • Wong T.K.T.
      • Lee W.W.J.
      • Chan E.W.Y.
      • Choi E.P.H.
      • Chui C.S.L.
      • Ip M.S.M.
      • Lau W.C.S.
      • Lau K.K.
      • Lee S.F.
      • Wong I.C.K.
      • Yu E.Y.T.
      • Lam C.L.K.
      The spill-over impact of the novel coronavirus-19 pandemic on medical care and disease outcomes in non-communicable diseases: a narrative review.
      ]. Second, after the first vaccine became available to the public in November 2020, governments began to relax restrictions that could be affecting disease management patterns for diabetes patients once again. Third, despite adjusting the heterogeneity and publication bias, the intrinsic difference between the study population and the small study effect suggests sensitivity and caution in interpreting the findings and drawing definitive conclusions. Lastly, we did not include studies that measured other non-diabetes-specific complications or comorbidities, such as mental health. Depression, anxiety, and stress are important determinants affecting disease management, particularly self-care behaviors. Future research is needed to understand the effect of mental health during the prolonged period of the pandemic.

      5. Conclusion

      The changes in some routine clinical indicators for T2DM during the COVID-19 pandemic were insignificant compared with the pre-pandemic period, besides total cholesterol and triglyceride, which showed an improvement and an impairment, respectively. Future studies are required to understand the extent to which disruptions to care and usual routines may have affected diabetes control during the pandemic so that we can produce information for strategy development to guide adequate care for those who suffered from the pandemic and to prepare for future health emergencies.

      Funding

      This work was supported by the Hong Kong Food and Health Bureau Commissioned Research on the Novel Coronavirus Disease (COVID-19) [Grant numbers: COVID19F08].

      Ethics approval and consent to participate

      Not applicable.

      Consent for publication

      Not applicable.

      CRediT authorship contribution statement

      Zhuoran Hu: Investigation, Validation, Visualization, Formal analysis, Investigation, Writing – original draft; Hin Moi Youn: Formal analysis, Validation, Investigation, Writing – review & editing; Lily Luk Siu Lee: Validation, Investigation; Esther Yee Tak Yu, Ivy Lynn Mak, David Vai-Kiong Chao, Welchie Wai Kit Ko, Gary Kui Kai Lau, Chak Sing Lau: Writing – review & editing; Ian Chi Kei Wong, Jianchao Quan: Writing – Supervision, Funding acquisition, Writing – review & editing; Eric Yuk Fai Wan Wan and Cindy Lo Kuen Lam: Supervision, Conceptualization, Methodology, Funding acquisition, Writing – review & editing. All authors read and approved the final manuscript.

      Declaration of Competing Interest

      ICKW reports research funding from Amgen, Bristol Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, the Hong Kong Research Grants Council, the Hong Kong Health and Medical Research Fund, the National Institute for Health Research in England, the European Commission, and the National Health and Medical Research Council in Australia, outside the submitted work; and is a non-executive director of Jacobson Medical in Hong Kong and a consultant to IQVIA and World Health Organization. Other authors declare that they have no competing interests.

      Acknowledgments

      None.

      Appendix A. Supplementary material

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